Deep learning-based beat-to-beat arterial blood pressure estimation using distant radar signals

• Published in: Neural computing & applications Vol. 37; no. 21; pp. 16677 - 16702
• Main Authors: Chowdhury, Farhana Ahmed, Hosain, Md Kamal, Hossain, Md Shafayet, Chowdhury, Muhammad E. H., Mahmud, Sakib, Kabir, Muhammad Ashad, Alqahtani, Abdulrahman, Hasan, Anwarul
• Format: Journal Article
• Language: English
• Published: London Springer London 01.07.2025; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Artificial neural networks; Biosensors; Blood pressure; Cardiovascular disease; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Datasets; Deep learning; Electrocardiography; Ground truth; Heart; Hypertension; Image Processing and Computer Vision; Machine learning; Methods; Neural networks; Original Article; Patients; Physiology; Probability and Statistics in Computer Science; Qualitative analysis; Radar data; Remote monitoring; Segmentation; Signal processing; Synthesis; Telemedicine; Waveforms; Wearable technology; Deep learning; Arterial blood pressure (ABP); Radar signals; Convolutional neural network (CNN); One-dimensional segmentation; MultiResLinkNet

Maintaining constant vigilance over arterial blood pressure (ABP) is crucial for diagnosing hypertension and other critical cardiovascular diseases. While traditional cuff-based approaches are non-invasive, they have limitations in providing continuous blood pressure monitoring. In contrast, complex...